General Projective Maps for Multidimensional Data Projection
General Projective Maps for Multidimensional Data Projection
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DOI：
10.1111/cgf.12845
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发表时间：
2016-05
影响因子：
2.5
通讯作者：
D. Lehmann;H. Theisel
中科院分区：
文献类型：
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作者：
D. Lehmann;H. Theisel
要将高维数据投影到2D域，有两种成熟的方法：RadViz和星星坐标。两者都在准确性，完整性，扭曲和相互作用问题方面得到了很好的探索。我们提出了一个概括的RadViz和星星坐标，使它统一这两种方法。我们通过考虑所有射影投影的空间来做到这一点。这提供了额外的自由度，我们将其用于三件事：首先，我们定义了RadViz和星星坐标之间的平滑过渡，允许用户利用这两种方法的优点。其次，我们定义了一个数据依赖的魔术透镜来探索数据。第三，我们优化了新的自由度，以尽量减少失真。我们将我们的方法应用于一些高维基准数据集。
To project high‐dimensional data to a 2D domain, there are two well‐established classes of approaches: RadViz and Star Coordinates. Both are well‐explored in terms of accuracy, completeness, distortions, and interaction issues. We present a generalization of both RadViz and Star Coordinates such that it unifies both approaches. We do so by considering the space of all projective projections. This gives additional degrees of freedom, which we use for three things: Firstly, we define a smooth transition between RadViz and Star Coordinates allowing the user to exploit the advantages of both approaches. Secondly, we define a data‐dependent magic lens to explore the data. Thirdly, we optimize the new degrees of freedom to minimize distortion. We apply our approach to a number of high‐dimensional benchmark datasets.